Multilevel bridge assessment based on InSAR data

Abstract

In the Netherlands, there is a significant backlog of infrastructure maintenance and deferred projects, exacerbated by the increasing demand for infrastructure renovation funds due to bridges reaching the end of their life cycles. Monitoring and assessing bridges typically involve visual inspections, which are subjective, expensive, and time-consuming. Remote sensing techniques, particularly Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR), offer a quick and objective solution for monitoring and analysis. MT-InSAR shows promise for low-cost, network-wide continuous structural monitoring. However, there is no defined assessment method with specific damage indicators linked to bridge failures that can be applied to various types of bridges. This thesis presents a weighted multilevel assessment method using MT-InSAR techniques. In the first level, four damage indicators (DIs) are proposed and analyzed on a point spatial scale: velocity DI, relative velocity DI, six-month velocity DI, and deviation from mean time series DI. In the second level, three DIs are proposed and analyzed on a grid cell spatial scale: velocity DI, relative velocity DI, and mean cumulative displacement DI. In the final level, two DIs are proposed and analyzed along the length of the bridge: largest differential displacement DI and deflection ratio DI. This method is applied to two bridges, each facing distinct issues: settlement and fatigue. The main findings demonstrate that the proposed damage indicators can be effectively utilized in the assessment system to qualitative rate the two case studies. The assessments reveal slow downward movements and large localized upward movements, which characterize the distinct underlying issues. The capabilities of the assessment method show promise for simultaneously evaluating various types of bridge. Additionally, it was found that imposing a coherence (quality) constraint on the MT-InSAR data is not advisable, as it may filter out the most critical data. Furthermore, the data indicates a potential seasonal unwrapping error, significantly affecting the analysis. The proposed method confirms the potential capabilities of MT-InSAR techniques in bridge assessment and suggests that this approach could serve as a foundation for network-level bridge assessments in the future, contributing to a much-needed early warning system.